integration-tests/examples/test_templates/deeplearning/template_visualization_tensor.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, cv2\n",
    "from keras.models import load_model\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pathes to model and weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_model = '/home/datalab-user/model_1000.json'\n",
    "path_to_weights = '/home/datalab-user/weigths_1000.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading test images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROWS = 128\n",
    "COLS = 128\n",
    "CHANNELS = 3\n",
    "TEST_DIR = '/home/datalab-user/test/'\n",
    "all_image_names = os.listdir(TEST_DIR)\n",
    "all_image_names.sort()\n",
    "test_images =  [TEST_DIR+i for i in all_image_names[6:11] + all_image_names[19:32] + all_image_names[33:34]]\n",
    "\n",
    "def read_image(file_path):\n",
    "    img = cv2.imread(file_path, cv2.IMREAD_COLOR)\n",
    "    return cv2.resize(img, (ROWS, COLS), interpolation=cv2.INTER_CUBIC).reshape(ROWS, COLS, CHANNELS)\n",
    "\n",
    "def prep_data(images):\n",
    "    count = len(images)\n",
    "    data = np.ndarray((count, ROWS, COLS, CHANNELS), dtype=np.uint8)\n",
    "\n",
    "    for i, image_file in enumerate(images):\n",
    "        image = read_image(image_file)\n",
    "        data[i] = image\n",
    "    return data\n",
    "test = prep_data(test_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the model and making predictions on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.device('/cpu:0'):\n",
    "    model = load_model(path_to_model)\n",
    "    model.load_weights(path_to_weights)\n",
    "    predictions = model.predict(test, verbose=2)     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing results (rendering can take about a minute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16, 12))\n",
    "for i in range(0, 12):\n",
    "    plt.subplot(3, 4, i+1)\n",
    "    if predictions[i, 0] >= 0.5: \n",
    "        plt.title('{:.2%} Dog'.format(predictions[i][0]))\n",
    "    else: \n",
    "        plt.title('{:.2%} Cat'.format(1-predictions[i][0]))\n",
    "        \n",
    "    plt.imshow(cv2.cvtColor(test[i], cv2.COLOR_BGR2RGB))\n",
    "    plt.axis('off')\n",
    "plt.show()"
   ]
  }
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